Study on the spatial distribution and inversion of forest ecosystem stocks play a crucial role in carbon stock estimation,biodiversity and global climate change research.However,due to the diversity of forest vegetation types,especially in tropical primary forest areas that are beyond the reach of human,forest survey data is missing,the estimates and inversions of forest stocks still present significant challenges.This study takes the tropical primitive rain forest area of 18.80 million ha in the West Syepik Province of Papua New Guinea as the study area,and uses the high-resolution remote sensing images of RapidEye,QuickBird and Landsat TM combining the field survey data to classify the land cover types in the study area.Based on the forest vegetation parameter information obtained by remote sensing image,the remote sensing inversion model of forest stock quantity is established in cooperation with the ground sample plot.The optimal inversion model is selected to estimate the forest stock volume,and combined with GIS technology to analyze the spatial distribution characteristics of the small class scale.The results show that the land cover types in the study area can be divided into low-altitude plain forests,low-altitude highland forests,low-mountain forests,sparse forests,swamp forests and other types,with a classification accuracy of 79.2%.The multivariate regression model R² of the stock volume remote sensing inversion model is 0.694,which has a good inversion accuracy for the forest stock volume.The distribution of forest stocks in the study area is characterized by a higher central area than the surrounding,northern and central eastern regions,which is significantly higher than the northwest and southeast regions,which corresponds to the distribution of land cover types in the study area.The forest stock inversion model used has important reference value for the estimation of forest resource stocks in tropical forest areas.